library(Seurat)
library(ggplot2)
library(patchwork)
library(knitr)
library(dplyr)
control_data <- Read10X(data.dir = 'GSE276251_RAW/control/')
control <- CreateSeuratObject(counts = control_data, project = 'control', min.cells = 3, min.features = 200)
high_glucose_data <- Read10X(data.dir = 'GSE276251_RAW/high_glucose/')
high_glucose <- CreateSeuratObject(counts = high_glucose_data, project = 'high_glucose', min.cells = 3, min.features = 200)
# combine CONTROL and HIGH GLUCOSE
combined <- merge(control, y = high_glucose, add.cell.ids = c("Control", "HighGlucose"))
head(combined@meta.data, 5)
## orig.ident nCount_RNA nFeature_RNA
## Control_AAACCCAAGACCAGCA-1 control 20307 3155
## Control_AAACCCAAGCCTGGAA-1 control 9374 1963
## Control_AAACCCACACACCTAA-1 control 10715 2117
## Control_AAACCCACAGAACATA-1 control 6655 1959
## Control_AAACCCAGTGCCCAGT-1 control 5792 1793
# calculate mitochondrial percentages
combined[["percent.mt"]] <- PercentageFeatureSet(combined, pattern = "^mt-")
head(combined@meta.data, 5)
## orig.ident nCount_RNA nFeature_RNA percent.mt
## Control_AAACCCAAGACCAGCA-1 control 20307 3155 3.210716
## Control_AAACCCAAGCCTGGAA-1 control 9374 1963 1.568167
## Control_AAACCCACACACCTAA-1 control 10715 2117 3.070462
## Control_AAACCCACAGAACATA-1 control 6655 1959 1.066867
## Control_AAACCCAGTGCCCAGT-1 control 5792 1793 5.231354
# create violin plots
VlnPlot(combined, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)
# plot mitochondria percentage and feature counts against RNA counts
plot1 <- FeatureScatter(combined, feature1 = "nCount_RNA", feature2 = "percent.mt") + scale_y_continuous(breaks = seq(0, 100, by = 5)) + geom_point(alpha = 0.05)
plot2 <- FeatureScatter(combined, feature1 = "nCount_RNA", feature2 = "nFeature_RNA") + scale_y_continuous(breaks = seq(0, 8500, by = 500)) + geom_point(alpha = 0.05)
plot1 + plot2
Discussion: Based on the plots, I decided to choose these filtration parameters. I applied the same filtration on CONTROL and HIGH GLUCOSE for comparability. High percentage of mitochonrial percentage indicate that the cell is stressed/dying. Therefore, filtering high mitochondrial percentage will ensure that DEGs observed would be due to biological variation rather than data variability.
# filter meta.data based on the conditions
combined <- subset(combined, subset = nFeature_RNA > 200 & nFeature_RNA < 7000 & percent.mt < 10)
SCTransform, as mentioned in the paper by Choudhary et al., replaces the need to run NormalizeData, FindVariableFeatures, and ScaleData separately. It also tackles with confounding due to mitochondrial percentage during normalizatoin. However, I decided not to use SCTransform.
# normalize data to reduce the effect of highly expressed genes
combined <- NormalizeData(combined, normalization.method = "LogNormalize", scale.factor = 10000)
# calculate a subset of features that exhibit high cell-to-cell variation
combined <- FindVariableFeatures(combined, selection.method = "vst", nfeatures = 2000)
# scale the data for PCA
# scaling performs linear transformation to get mean of 0 and variation of 1
all_genes_combined <- rownames(combined)
combined <- ScaleData(combined, features = all_genes_combined)
# perform PCA
combined <- RunPCA(combined, features = VariableFeatures(object = combined))
# analyze elbow plot to find optimal PC dimension
ElbowPlot(combined, ndims=50) + ggtitle('Elbowplot of PC Dimension vs. Std Dev')
I chose 20 as the elbow point. Although 10 PCs is usually picked, but based on this elbowplot I chose 20 PCs, as up to this point we can expect to gain significant information.
Additionally, I used the clustering resolution at 0.4, which is crucial for accurate interpretation.
# perform clustering
uninteg <- FindNeighbors(combined, dims = 1:20, reduction = "pca")
uninteg <- FindClusters(uninteg, resolution = .4, cluster.name = "unintegrated_clusters")
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 11938
## Number of edges: 420191
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.9388
## Number of communities: 22
## Elapsed time: 1 seconds
# perform UMAP for visualization
uninteg <- RunUMAP(uninteg, dims = 1:20, reduction = "pca", reduction.name = "umap.unintegrated")
## 02:19:06 UMAP embedding parameters a = 0.9922 b = 1.112
## 02:19:06 Read 11938 rows and found 20 numeric columns
## 02:19:06 Using Annoy for neighbor search, n_neighbors = 30
## 02:19:06 Building Annoy index with metric = cosine, n_trees = 50
## 0% 10 20 30 40 50 60 70 80 90 100%
## [----|----|----|----|----|----|----|----|----|----|
## **************************************************|
## 02:19:07 Writing NN index file to temp file /var/folders/1k/jj40mz3j3zj2w9gsgg7xpylm0000gn/T//RtmpWjRnAu/file9d8920272da2
## 02:19:07 Searching Annoy index using 1 thread, search_k = 3000
## 02:19:10 Annoy recall = 100%
## 02:19:10 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
## 02:19:10 Initializing from normalized Laplacian + noise (using RSpectra)
## 02:19:11 Commencing optimization for 200 epochs, with 519932 positive edges
## 02:19:15 Optimization finished
# visulize clusters
DimPlot(uninteg, reduction = "umap.unintegrated", label = TRUE) + ggtitle('Dimplot of clusters')
# integrate the splits on CONTROL and HIGH GLUCOSE
integ <- IntegrateLayers(object = combined, method = CCAIntegration, orig.reduction = "pca", new.reduction = "integrated.cca",
verbose = FALSE)
# join the integrated layers
integ[["RNA"]] <- JoinLayers(integ[["RNA"]])
# perform clustering
integ <- FindNeighbors(integ, reduction = "integrated.cca", dims = 1:20)
integ <- FindClusters(integ, resolution = .4)
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 11938
## Number of edges: 427051
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.9383
## Number of communities: 20
## Elapsed time: 1 seconds
# perform UMAP for visualization
integ <- RunUMAP(integ, dims = 1:20, reduction = "integrated.cca")
## 02:20:59 UMAP embedding parameters a = 0.9922 b = 1.112
## 02:20:59 Read 11938 rows and found 20 numeric columns
## 02:20:59 Using Annoy for neighbor search, n_neighbors = 30
## 02:20:59 Building Annoy index with metric = cosine, n_trees = 50
## 0% 10 20 30 40 50 60 70 80 90 100%
## [----|----|----|----|----|----|----|----|----|----|
## **************************************************|
## 02:21:00 Writing NN index file to temp file /var/folders/1k/jj40mz3j3zj2w9gsgg7xpylm0000gn/T//RtmpWjRnAu/file9d89549bf4b8
## 02:21:00 Searching Annoy index using 1 thread, search_k = 3000
## 02:21:02 Annoy recall = 100%
## 02:21:03 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
## 02:21:03 Initializing from normalized Laplacian + noise (using RSpectra)
## 02:21:04 Commencing optimization for 200 epochs, with 521170 positive edges
## 02:21:08 Optimization finished
# visualization
DimPlot(integ, reduction = "umap", label = TRUE) + ggtitle('Dimplot of clusters after integration')
I decided not to use pseudobulk approach using AggregateExpression. Rather I created my own object to contain all the DEGs for all exhaustive cluster-condition pairs.
Furthermore, I kept the clusters as (0,1,2,3,..) instead of naming them as mentioned.
# create a new column that combines cluster and group (control/high glucose) information
integ$cond_cluster <- paste(Idents(integ), ifelse(integ$orig.ident == "control", "Con", "HG"), sep = '_')
Idents(integ) <- integ$cond_cluster
# visualize CONTROL and HIGH GLUCOSE separately
DimPlot(integ, reduction='umap', label = TRUE, split.by = 'orig.ident') + ggtitle('Dimplot of clusters by control(Con) and high glucose(HG) group')
# check the indentifiers
table(Idents(integ))
##
## 2_Con 5_Con 1_Con 10_Con 15_Con 0_Con 9_Con 4_Con 11_Con 16_Con 3_Con
## 862 395 1403 224 163 2124 202 494 173 113 573
## 13_Con 14_Con 17_Con 12_Con 7_Con 8_Con 6_Con 19_Con 18_Con 2_HG 1_HG
## 153 155 35 227 288 281 270 25 38 317 281
## 10_HG 0_HG 5_HG 9_HG 3_HG 13_HG 4_HG 18_HG 14_HG 7_HG 11_HG
## 108 994 184 156 404 91 300 26 65 186 133
## 8_HG 19_HG 17_HG 6_HG 16_HG 12_HG 15_HG
## 79 21 64 248 38 20 25
# List for contating DEGs for each cluster groups
top_DEGs_by_cluster_condition <- list()
# get unique clusters and conditions
clusters <- unique(integ$seurat_clusters)
conditions <- unique(integ$orig.ident)
I only did control_# vs high_glucose_# since the absolute avg_log2FC values are the same if they are flipped i.e. if the control and high_glucose groups are flipped in FindMarkers, the signs of avg_log2FC values get flipped. Therefore, I decided not to include both directions for simplicity.
In my analysis I am calculating the 10 most differentially expressed genes (DEGs) regardless of direction (positive or negative). The values are sorted based on the absolute values of avg_log2FC, however I am keeping their signed value for relaying the mutual direction.
# calculate DEGs for cluster-condition pairs
for (cluster in clusters) {
# create identifiers for the cluster groups
ident.1 <- paste(cluster, 'Con', sep = "_")
ident.2 <- paste(cluster, 'HG', sep = "_")
# find markers between the two conditions in the same cluster
markers <- FindMarkers(integ, ident.1 = ident.1, ident.2 = ident.2)
# get top genes based on highest absolute avg_log2FC
top_genes <- head(markers[order(-abs(markers$avg_log2FC)), ], n = 10)
top_DEGs_by_cluster_condition[[paste("Cluster", cluster, "CONTROL", "and GLUCOSE")]] <- top_genes
}
# print the top 10 DEGs for clusters (0,1,2,3,...)
for (name in names(top_DEGs_by_cluster_condition)) {
# print the table
knitr::kable(top_DEGs_by_cluster_condition[[name]], caption = name) %>%
print()
cat("\n\n")
}
##
##
## Table: Cluster 2 CONTROL and GLUCOSE
##
## | | p_val| avg_log2FC| pct.1| pct.2| p_val_adj|
## |:----------------|--------:|----------:|-----:|-----:|---------:|
## |histh1l | 0.000000| -7.766887| 0.114| 0.978| 0.0000000|
## |scpp5 | 0.000000| -7.645310| 0.000| 0.073| 0.0000000|
## |nptna | 0.000000| 7.366504| 0.201| 0.000| 0.0000000|
## |zgc:114046 | 0.000000| -7.288518| 0.001| 0.129| 0.0000000|
## |CABZ01084501.3 | 0.000000| -6.815843| 0.000| 0.038| 0.0002296|
## |h1f0 | 0.000000| -6.712603| 0.085| 0.915| 0.0000000|
## |h1fx | 0.000000| -6.405373| 0.092| 0.571| 0.0000000|
## |hist1h2a10 | 0.000541| -6.193395| 0.002| 0.022| 1.0000000|
## |BX004769.1 | 0.000000| -6.152722| 0.000| 0.035| 0.0009564|
## |si:dkey-237h12.3 | 0.000000| -6.141908| 0.001| 0.060| 0.0000001|
##
##
##
##
## Table: Cluster 5 CONTROL and GLUCOSE
##
## | | p_val| avg_log2FC| pct.1| pct.2| p_val_adj|
## |:-----------------|---------:|----------:|-----:|-----:|---------:|
## |si:dkey-203a12.7 | 0.0000981| -6.014148| 0.000| 0.038| 1.0000000|
## |mef2b | 0.0383366| -5.631471| 0.000| 0.011| 1.0000000|
## |CR352342.1 | 0.0000000| -5.197330| 0.010| 0.245| 0.0000000|
## |ccl34a.4 | 0.0000007| -5.026317| 0.003| 0.071| 0.0162787|
## |si:dkey-203a12.2 | 0.0626976| -4.953005| 0.003| 0.016| 1.0000000|
## |zgc:92287 | 0.0000000| -4.703731| 0.003| 0.147| 0.0000000|
## |CU927934.3 | 0.0033236| -4.630599| 0.000| 0.022| 1.0000000|
## |si:dkey-85n7.7 | 0.0000000| -4.391621| 0.008| 0.136| 0.0000006|
## |hspb11 | 0.0001924| -4.379120| 0.010| 0.065| 1.0000000|
## |si:ch211-106j24.1 | 0.0000675| -4.366037| 0.003| 0.049| 1.0000000|
##
##
##
##
## Table: Cluster 1 CONTROL and GLUCOSE
##
## | | p_val| avg_log2FC| pct.1| pct.2| p_val_adj|
## |:-----------------|---------:|----------:|-----:|-----:|---------:|
## |h1f0 | 0.0000000| -6.942986| 0.068| 0.904| 0|
## |histh1l | 0.0000000| -6.870069| 0.133| 0.964| 0|
## |BX511021.1 | 0.0000000| -6.557072| 0.000| 0.057| 0|
## |scpp5 | 0.0000000| -6.486432| 0.000| 0.064| 0|
## |si:ch211-191i18.2 | 0.0000000| -6.478975| 0.001| 0.075| 0|
## |c1qtnf5 | 0.0000000| -6.388616| 0.001| 0.046| 0|
## |si:ch73-21g5.7 | 0.0017437| -6.183092| 0.001| 0.011| 1|
## |zgc:110425 | 0.0000000| -5.908222| 0.006| 0.310| 0|
## |hbae5 | 0.0000000| -5.762707| 0.001| 0.046| 0|
## |h1fx | 0.0000000| -5.735182| 0.065| 0.580| 0|
##
##
##
##
## Table: Cluster 10 CONTROL and GLUCOSE
##
## | | p_val| avg_log2FC| pct.1| pct.2| p_val_adj|
## |:---------------|---------:|----------:|-----:|-----:|---------:|
## |rhag | 0.0000007| 5.776396| 0.232| 0.019| 0.0156523|
## |igf2a | 0.0000035| 5.663051| 0.237| 0.037| 0.0848324|
## |lgals1l1 | 0.0000000| -5.619188| 0.000| 0.194| 0.0000002|
## |f8 | 0.0000265| 5.534494| 0.165| 0.009| 0.6373231|
## |gsnb | 0.0038503| -5.532871| 0.000| 0.037| 1.0000000|
## |si:dkey-33i11.4 | 0.0418286| -5.508814| 0.000| 0.019| 1.0000000|
## |cldn15la | 0.0124806| -5.335335| 0.000| 0.028| 1.0000000|
## |CR788316.3 | 0.0418286| -5.287044| 0.000| 0.019| 1.0000000|
## |si:dkey-21e2.13 | 0.0418286| -5.185048| 0.000| 0.019| 1.0000000|
## |col9a3 | 0.0038503| -5.165806| 0.000| 0.037| 1.0000000|
##
##
##
##
## Table: Cluster 15 CONTROL and GLUCOSE
##
## | | p_val| avg_log2FC| pct.1| pct.2| p_val_adj|
## |:----------------|--------:|----------:|-----:|-----:|---------:|
## |h1f0 | 0.00e+00| -8.778690| 0.043| 1.00| 0.0000000|
## |h1fx | 0.00e+00| -8.172415| 0.012| 0.56| 0.0000000|
## |anxa2a | 2.91e-05| 7.266089| 0.460| 0.00| 0.7004526|
## |ehd2b | 0.00e+00| 6.929978| 0.804| 0.04| 0.0000021|
## |histh1l | 0.00e+00| -6.873653| 0.147| 1.00| 0.0000000|
## |ndst3 | 0.00e+00| -6.310998| 0.000| 0.20| 0.0001976|
## |slc16a9a | 0.00e+00| -6.095494| 0.006| 0.28| 0.0000072|
## |si:dkey-148a17.5 | 9.10e-06| -5.962627| 0.000| 0.12| 0.2190985|
## |her12 | 0.00e+00| -5.848875| 0.012| 0.36| 0.0000001|
## |bcr | 0.00e+00| -5.832498| 0.000| 0.32| 0.0000000|
##
##
##
##
## Table: Cluster 0 CONTROL and GLUCOSE
##
## | | p_val| avg_log2FC| pct.1| pct.2| p_val_adj|
## |:----------|--------:|----------:|-----:|-----:|---------:|
## |scpp5 | 0.00e+00| -9.285502| 0.000| 0.060| 0.0000000|
## |CR848032.1 | 0.00e+00| 8.964428| 0.032| 0.000| 0.0002843|
## |cadm1b | 0.00e+00| -7.682640| 0.000| 0.067| 0.0000000|
## |hist1h4l.2 | 0.00e+00| -7.359451| 0.000| 0.016| 0.0001106|
## |hpgd | 0.00e+00| 7.329467| 0.040| 0.000| 0.0000039|
## |CR936442.1 | 0.00e+00| -7.063449| 0.008| 0.366| 0.0000000|
## |kcnk10a | 0.00e+00| -6.918316| 0.000| 0.033| 0.0000000|
## |CT573342.1 | 0.00e+00| -6.917578| 0.000| 0.042| 0.0000000|
## |cyp24a1 | 1.34e-05| 6.874969| 0.019| 0.000| 0.3218985|
## |cyp1c1 | 0.00e+00| 6.704126| 0.134| 0.010| 0.0000000|
##
##
##
##
## Table: Cluster 9 CONTROL and GLUCOSE
##
## | | p_val| avg_log2FC| pct.1| pct.2| p_val_adj|
## |:----------------|---------:|----------:|-----:|-----:|---------:|
## |opn1mw1 | 0.0020016| 11.498681| 0.059| 0.000| 1.00e+00|
## |epd | 0.2147715| 7.913465| 0.010| 0.000| 1.00e+00|
## |pde6ha | 0.0002359| 7.189619| 0.144| 0.032| 1.00e+00|
## |si:ch211-133n4.6 | 0.0780432| 7.051485| 0.020| 0.000| 1.00e+00|
## |grk1b | 0.0002118| 6.594820| 0.084| 0.000| 1.00e+00|
## |aqp8a.1 | 0.0020016| 6.529552| 0.059| 0.000| 1.00e+00|
## |si:dkeyp-57d7.4 | 0.0003323| 6.403866| 0.079| 0.000| 1.00e+00|
## |isg15 | 0.0000000| -6.390415| 0.005| 0.186| 2.33e-05|
## |rcvrn3 | 0.0001259| 6.366964| 0.129| 0.019| 1.00e+00|
## |cyp26a1 | 0.0054665| -6.353971| 0.005| 0.051| 1.00e+00|
##
##
##
##
## Table: Cluster 4 CONTROL and GLUCOSE
##
## | | p_val| avg_log2FC| pct.1| pct.2| p_val_adj|
## |:--------------|---------:|----------:|-----:|-----:|---------:|
## |vipb | 0.0261543| -8.461840| 0.000| 0.010| 1.0000000|
## |fbp2 | 0.6004456| 7.505629| 0.014| 0.010| 1.0000000|
## |scpp5 | 0.0000000| -7.411119| 0.000| 0.067| 0.0001524|
## |CABZ01021592.1 | 0.0643362| -7.247476| 0.004| 0.017| 1.0000000|
## |h1f0 | 0.0000000| -6.185093| 0.107| 0.887| 0.0000000|
## |her7 | 0.2835699| 6.163228| 0.010| 0.003| 1.0000000|
## |aoc1 | 0.0032910| 6.012152| 0.028| 0.000| 1.0000000|
## |apoc1 | 0.7746374| 5.996635| 0.012| 0.010| 1.0000000|
## |vgf | 0.0002678| -5.962332| 0.000| 0.027| 1.0000000|
## |histh1l | 0.0000000| -5.837142| 0.180| 0.960| 0.0000000|
##
##
##
##
## Table: Cluster 11 CONTROL and GLUCOSE
##
## | | p_val| avg_log2FC| pct.1| pct.2| p_val_adj|
## |:----------|---------:|----------:|-----:|-----:|---------:|
## |epd | 0.2159569| 6.564029| 0.012| 0.000| 1.0000000|
## |ggctb | 0.4091576| 5.660247| 0.029| 0.015| 1.0000000|
## |themis | 0.0000000| -5.637038| 0.000| 0.233| 0.0000006|
## |zgc:173593 | 0.0000150| 5.354153| 0.225| 0.053| 0.3604702|
## |acta1a | 0.0103457| -5.331859| 0.000| 0.038| 1.0000000|
## |h1f0 | 0.0000000| -5.077892| 0.029| 0.414| 0.0000000|
## |zgc:154164 | 0.0000014| -5.055851| 0.012| 0.158| 0.0337854|
## |gngt2b | 0.0305646| 4.952000| 0.035| 0.000| 1.0000000|
## |tnfb | 0.0482158| -4.942845| 0.006| 0.038| 1.0000000|
## |cdh1 | 0.0023281| -4.934184| 0.000| 0.053| 1.0000000|
##
##
##
##
## Table: Cluster 16 CONTROL and GLUCOSE
##
## | | p_val| avg_log2FC| pct.1| pct.2| p_val_adj|
## |:-----------------|---------:|----------:|-----:|-----:|---------:|
## |si:dkey-7i4.24 | 0.0000959| -7.539744| 0.000| 0.132| 1.0000000|
## |CELA1 (1 of many) | 0.2445315| 7.277708| 0.035| 0.000| 1.0000000|
## |il12a | 0.0000000| -7.136934| 0.000| 0.395| 0.0000001|
## |ctrb1 | 0.7818411| 7.073770| 0.062| 0.053| 1.0000000|
## |prss59.2 | 0.6159546| 7.005067| 0.053| 0.079| 1.0000000|
## |prss1 | 0.4874345| 6.996601| 0.053| 0.026| 1.0000000|
## |cxcl11.1 | 0.0000314| -5.993627| 0.018| 0.211| 0.7556562|
## |si:ch211-277c7.6 | 0.0027187| -5.854823| 0.000| 0.079| 1.0000000|
## |zgc:153317 | 0.0027187| -5.827128| 0.000| 0.079| 1.0000000|
## |prss59.1 | 0.7714452| 5.793364| 0.035| 0.026| 1.0000000|
##
##
##
##
## Table: Cluster 3 CONTROL and GLUCOSE
##
## | | p_val| avg_log2FC| pct.1| pct.2| p_val_adj|
## |:---------------|---------:|----------:|-----:|-----:|---------:|
## |h1f0 | 0.0000000| -8.658662| 0.023| 0.871| 0|
## |histh1l | 0.0000000| -7.413053| 0.148| 0.998| 0|
## |CR352342.1 | 0.0000000| -7.185430| 0.000| 0.250| 0|
## |tat | 0.0000000| -6.374281| 0.010| 0.111| 0|
## |slc16a9b | 0.0000000| -6.254767| 0.003| 0.475| 0|
## |apoc1 | 0.1436038| 6.113795| 0.010| 0.002| 1|
## |si:ch73-127m5.2 | 0.0000000| -6.030842| 0.002| 0.376| 0|
## |zgc:154164 | 0.0000000| -5.692307| 0.005| 0.205| 0|
## |opn1sw1 | 0.0012290| 5.594181| 0.031| 0.002| 1|
## |h1fx | 0.0000000| -5.463329| 0.312| 0.953| 0|
##
##
##
##
## Table: Cluster 13 CONTROL and GLUCOSE
##
## | | p_val| avg_log2FC| pct.1| pct.2| p_val_adj|
## |:---------------|---------:|----------:|-----:|-----:|---------:|
## |h1f0 | 0.0000000| -8.083851| 0.026| 0.538| 0.0000000|
## |histh1l | 0.0000000| -8.062522| 0.065| 0.912| 0.0000000|
## |si:dkey-31i7.2 | 0.0000000| 8.029419| 0.549| 0.000| 0.0000000|
## |zgc:194125 | 0.0244356| -7.271822| 0.000| 0.033| 1.0000000|
## |xkr8.2 | 0.0000000| 7.261492| 0.464| 0.000| 0.0000000|
## |h1fx | 0.0000000| -7.084017| 0.046| 0.418| 0.0000000|
## |zgc:163121 | 0.0000299| -6.971736| 0.000| 0.110| 0.7179203|
## |hbbe2 | 0.0000000| -6.714328| 0.013| 0.231| 0.0003393|
## |CABZ01021592.1 | 0.7186244| 6.416284| 0.007| 0.011| 1.0000000|
## |si:ch73-329n5.6 | 0.0244356| -6.280881| 0.000| 0.033| 1.0000000|
##
##
##
##
## Table: Cluster 14 CONTROL and GLUCOSE
##
## | | p_val| avg_log2FC| pct.1| pct.2| p_val_adj|
## |:-----------------|---------:|----------:|-----:|-----:|---------:|
## |h1f0 | 0.0000000| -7.896238| 0.045| 0.892| 0|
## |MFAP4 (1 of many) | 0.1249664| -7.518356| 0.000| 0.015| 1|
## |h1fx | 0.0000000| -6.667508| 0.226| 0.954| 0|
## |histh1l | 0.0000000| -6.525047| 0.290| 1.000| 0|
## |cabp2a | 0.0033099| -5.630721| 0.006| 0.077| 1|
## |slc16a9b | 0.0000000| -5.501857| 0.000| 0.385| 0|
## |lmod1b | 0.5251227| -5.283367| 0.006| 0.015| 1|
## |twf2b | 0.0291260| -5.212349| 0.000| 0.031| 1|
## |hbbe1.2 | 0.0105623| 5.088563| 0.174| 0.046| 1|
## |dlx3b | 0.1551856| -5.013838| 0.006| 0.031| 1|
##
##
##
##
## Table: Cluster 17 CONTROL and GLUCOSE
##
## | | p_val| avg_log2FC| pct.1| pct.2| p_val_adj|
## |:--------------|---------:|----------:|-----:|-----:|---------:|
## |CABZ01086611.1 | 0.0000760| 6.886297| 0.229| 0.000| 1.0000000|
## |zgc:110425 | 0.0000004| -6.772000| 0.029| 0.547| 0.0097027|
## |slitrk4 | 0.0185838| 6.763753| 0.086| 0.000| 1.0000000|
## |whrna | 0.0020951| 6.546459| 0.143| 0.000| 1.0000000|
## |CR848032.1 | 0.1831502| 6.495815| 0.029| 0.000| 1.0000000|
## |FQ311928.1 | 0.0000245| 6.404022| 0.257| 0.000| 0.5886409|
## |hbbe2 | 0.0006201| -6.403105| 0.000| 0.281| 1.0000000|
## |tiam1a | 0.0062303| 6.241831| 0.114| 0.000| 1.0000000|
## |slc6a6b | 0.0185838| 6.071217| 0.086| 0.000| 1.0000000|
## |DNAJA4 | 0.0020951| 6.044160| 0.143| 0.000| 1.0000000|
##
##
##
##
## Table: Cluster 12 CONTROL and GLUCOSE
##
## | | p_val| avg_log2FC| pct.1| pct.2| p_val_adj|
## |:----------------|---------:|----------:|-----:|-----:|---------:|
## |ghrl | 0.0007961| -12.801160| 0.000| 0.05| 1.0000000|
## |FO834903.1 | 0.0000019| -8.692491| 0.000| 0.10| 0.0457258|
## |irg1l | 0.0000019| -8.691377| 0.000| 0.10| 0.0457258|
## |pyyb | 0.0000000| -8.538754| 0.000| 0.20| 0.0000003|
## |scpp5 | 0.0000000| -8.538272| 0.000| 0.15| 0.0001179|
## |BX855618.1 | 0.0007961| -8.440515| 0.000| 0.05| 1.0000000|
## |si:dkey-96g2.1 | 0.0000000| -8.336434| 0.000| 0.25| 0.0000000|
## |si:ch211-250m6.7 | 0.0000019| -8.162711| 0.000| 0.10| 0.0457258|
## |si:zfos-2372e4.1 | 0.0294993| -7.948652| 0.004| 0.05| 1.0000000|
## |si:dkey-31g6.6 | 0.0001951| -7.929023| 0.004| 0.10| 1.0000000|
##
##
##
##
## Table: Cluster 7 CONTROL and GLUCOSE
##
## | | p_val| avg_log2FC| pct.1| pct.2| p_val_adj|
## |:----------------|---------:|----------:|-----:|-----:|---------:|
## |zgc:172053 | 0.6599830| -7.833955| 0.007| 0.011| 1.0000000|
## |tcnl | 0.3308418| -7.737101| 0.003| 0.011| 1.0000000|
## |trpv6 | 0.0325070| 7.326812| 0.024| 0.000| 1.0000000|
## |cts12 | 0.1756536| -6.992091| 0.010| 0.027| 1.0000000|
## |cnfn | 0.0786512| -6.788365| 0.000| 0.011| 1.0000000|
## |s100a11 | 0.7722090| 6.558453| 0.076| 0.086| 1.0000000|
## |ccl19a.2 | 0.0000305| -6.553332| 0.000| 0.059| 0.7335690|
## |serpinb1l4 | 0.0039909| 6.316370| 0.056| 0.005| 1.0000000|
## |si:dkey-203a12.9 | 0.0325070| 6.245550| 0.024| 0.000| 1.0000000|
## |hbae5 | 0.0000130| -5.988825| 0.000| 0.065| 0.3130274|
##
##
##
##
## Table: Cluster 8 CONTROL and GLUCOSE
##
## | | p_val| avg_log2FC| pct.1| pct.2| p_val_adj|
## |:--------------|---------:|----------:|-----:|-----:|---------:|
## |myl7 | 0.0010685| -9.581991| 0.000| 0.038| 1.0000000|
## |cmlc1 | 0.0076725| -9.466156| 0.000| 0.025| 1.0000000|
## |si:dkey-21e2.8 | 0.0010685| -8.966881| 0.000| 0.038| 1.0000000|
## |myh7 | 0.0076725| -8.678389| 0.000| 0.025| 1.0000000|
## |nppa | 0.0100355| -8.675601| 0.004| 0.038| 1.0000000|
## |actc1a | 0.0001541| -8.115405| 0.000| 0.051| 1.0000000|
## |cxcl11.1 | 0.0000005| -8.064405| 0.000| 0.089| 0.0118424|
## |tnnt2a | 0.0000226| -7.897785| 0.000| 0.063| 0.5429001|
## |tnni1b | 0.0076725| -7.739598| 0.000| 0.025| 1.0000000|
## |myh7l | 0.0100355| -7.185845| 0.004| 0.038| 1.0000000|
##
##
##
##
## Table: Cluster 6 CONTROL and GLUCOSE
##
## | | p_val| avg_log2FC| pct.1| pct.2| p_val_adj|
## |:------------------|---------:|----------:|-----:|-----:|---------:|
## |pkd1l2a | 0.0548052| 7.366535| 0.015| 0.000| 1.0000000|
## |or131-2 | 0.0705280| -7.253483| 0.000| 0.012| 1.0000000|
## |si:ch211-196h16.12 | 0.0184047| 7.247488| 0.022| 0.000| 1.0000000|
## |isg15 | 0.0000007| -6.980641| 0.004| 0.097| 0.0179613|
## |pyya | 0.0795392| -6.830017| 0.004| 0.020| 1.0000000|
## |pde6gb | 0.0184047| 6.749597| 0.022| 0.000| 1.0000000|
## |s100s | 0.1898777| 6.616121| 0.022| 0.008| 1.0000000|
## |cxcl12b | 0.0315865| 6.594344| 0.019| 0.000| 1.0000000|
## |si:ch211-131k2.2 | 0.0967209| 6.562497| 0.011| 0.000| 1.0000000|
## |ociad2 | 0.0069393| -6.284127| 0.004| 0.036| 1.0000000|
##
##
##
##
## Table: Cluster 19 CONTROL and GLUCOSE
##
## | | p_val| avg_log2FC| pct.1| pct.2| p_val_adj|
## |:----------|---------:|----------:|-----:|-----:|---------:|
## |myhz1.1.1 | 0.2058382| 6.962104| 0.16| 0.048| 1.0000000|
## |smyhc1 | 0.9006766| -6.825976| 0.04| 0.048| 1.0000000|
## |gapdhs | 0.0977978| 6.276511| 0.28| 0.095| 1.0000000|
## |tnni4b.2 | 0.4396478| -5.899504| 0.04| 0.095| 1.0000000|
## |aldocb | 0.9399328| 5.661240| 0.12| 0.143| 1.0000000|
## |zgc:163073 | 0.2008024| 5.566123| 0.08| 0.000| 1.0000000|
## |dab2 | 0.0005044| -5.368806| 0.04| 0.476| 1.0000000|
## |fbp2 | 0.2667804| 5.354447| 0.20| 0.381| 1.0000000|
## |slc35f2 | 0.0566229| -5.338728| 0.00| 0.143| 1.0000000|
## |h1f0 | 0.0000282| -5.233460| 0.20| 0.762| 0.6780267|
##
##
##
##
## Table: Cluster 18 CONTROL and GLUCOSE
##
## | | p_val| avg_log2FC| pct.1| pct.2| p_val_adj|
## |:----------|---------:|----------:|-----:|-----:|---------:|
## |nphs2 | 0.2391449| -6.280273| 0.000| 0.038| 1.00e+00|
## |BX248318.1 | 0.2391449| -6.261792| 0.000| 0.038| 1.00e+00|
## |h1f0 | 0.0000000| -6.235230| 0.184| 0.885| 2.01e-05|
## |fabp7a | 0.0055072| -5.919455| 0.000| 0.192| 1.00e+00|
## |gcga | 0.7855553| -5.502415| 0.026| 0.038| 1.00e+00|
## |CR854839.1 | 0.0055072| -5.422181| 0.000| 0.192| 1.00e+00|
## |cldn7a | 0.0139057| -5.222504| 0.000| 0.154| 1.00e+00|
## |apodb | 0.7795511| 5.196010| 0.053| 0.038| 1.00e+00|
## |acta2 | 0.6600730| -4.980924| 0.053| 0.077| 1.00e+00|
## |histh1l | 0.0000000| -4.930170| 0.368| 1.000| 1.10e-06|